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//- 💫 DOCS > USAGE > VISUALIZERS
include ../../_includes/_mixins
p
| As of v2.0, our popular visualizers, #[+a(DEMOS_URL + "/displacy") displaCy]
| and #[+a(DEMOS_URL + "displacy-ent") displaCy #[sup ENT]] are finally an
| official part of the library. Visualizing a dependency parse or named
| entities in a text is not only a fun NLP demo – it can also be incredibly
| helpful in speeding up development and debugging your code and training
| process. Instead of printing a list of dependency labels or entity spans,
| you can simply pass your #[code Doc] objects to #[code displacy] and view
| the visualizations in your browser, or export them as HTML files or
| vector graphics. displaCy also comes with a #[+a("#jupyter") Jupyter hook]
| that returns the markup in a format ready to be rendered in a notebook.
+aside("What about the old visualizers?")
| Our JavaScript-based visualizers #[+src(gh("displacy")) displacy.js] and
| #[+src(gh("displacy-ent")) displacy-ent.js] will still be available on
| GitHub. If you're looking to implement web-based visualizations, we
| generally recommend using those instead of spaCy's built-in
| #[code displacy] module. It'll allow your application to perform all
| rendering on the client and only rely on the server for the text
| processing. The generated markup is also more compatible with modern web
| standards.
+h(2, "getting-started") Getting started
p
| The quickest way visualize #[code Doc] is to use
| #[+api("displacy#serve") #[code displacy.serve]]. This will spin up a
| simple web server and let you view the result straight from your browser.
| displaCy can either take a single #[code Doc] or a list of #[code Doc]
| objects as its first argument. This lets you construct them however you
| like – using any model or modifications you like.
+h(3, "dep") Visualizing the dependency parse
p
| The dependency visualizer, #[code dep], shows part-of-speech tags
| and syntactic dependencies.
+code("Dependency example").
import spacy
from spacy import displacy
nlp = spacy.load('en')
doc = nlp(u'This is a sentence.')
displacy.serve(doc, style='dep')
+codepen("f0e85b64d469d6617251d8241716d55f", 370)
p
| The argument #[code options] lets you specify a dictionary of settings
| to customise the layout, for example:
+table(["Name", "Type", "Description", "Default"])
+row
+cell #[code compact]
+cell bool
+cell "Compact mode" with square arrows that takes up less space.
+cell #[code False]
+row
+cell #[code color]
+cell unicode
+cell Text color (HEX, RGB or color names).
+cell #[code '#000000']
+row
+cell #[code bg]
+cell unicode
+cell Background color (HEX, RGB or color names).
+cell #[code '#ffffff']
+row
+cell #[code font]
+cell unicode
+cell Font name or font family for all text.
+cell #[code 'Arial']
p
| For a list of all available options, see the
| #[+api("displacy#options") #[code displacy] API documentation].
+aside-code("Options example").
options = {'compact': True, 'bg': '#09a3d5',
'color': 'white', 'font': 'Source Sans Pro'}
displacy.serve(doc, style='dep', options=options)
+codepen("39c02c893a84794353de77a605d817fd", 360)
+h(3, "ent") Visualizing the entity recognizer
p
| The entity visualizer, #[code ent], highlights named entities and
| their labels in a text.
+code("Named Entity example").
import spacy
from spacy import displacy
text = """But Google is starting from behind. The company made a late push
into hardware, and Apple’s Siri, available on iPhones, and Amazon’s Alexa
software, which runs on its Echo and Dot devices, have clear leads in
consumer adoption."""
nlp = spacy.load('custom_ner_model')
doc = nlp(text)
displacy.serve(doc, style='ent')
+codepen("a73f8b68f9af3157855962b283b364e4", 345)
p The entity visualizer lets you customise the following #[code options]:
+table(["Name", "Type", "Description", "Default"])
+row
+cell #[code ents]
+cell list
+cell
| Entity types to highlight (#[code None] for all types).
+cell #[code None]
+row
+cell #[code colors]
+cell dict
+cell
| Color overrides. Entity types in lowercase should be mapped to
| color names or values.
+cell #[code {}]
p
| If you specify a list of #[code ents], only those entity types will be
| rendered – for example, you can choose to display #[code PERSON] entities.
| Internally, the visualizer knows nothing about available entity types and
| will render whichever spans and labels it receives. This makes it
| especially easy to work with custom entity types. By default, displaCy
| comes with colours for all
| #[+a("/docs/api/annotation#named-entities") entity types supported by spaCy].
| If you're using custom entity types, you can use the #[code colors]
| setting to add your own colours for them.
+aside-code("Options example").
colors = {'ORG': 'linear-gradient(90deg, #aa9cfc, #fc9ce7)'}
options = {'ents': ['ORG'], 'colors': colors}
displacy.serve(doc, style='ent', options=options)
+codepen("f42ec690762b6f007022a7acd6d0c7d4", 300)
p
| The above example uses a little trick: Since the background colour values
| are added as the #[code background] style attribute, you can use any
| #[+a("https://tympanus.net/codrops/css_reference/background/") valid background value]
| or shorthand — including gradients and even images!
+h(2, "render") Rendering visualizations
p
| If you don't need the web server and just want to generate the markup
| – for example, to export it to a file or serve it in a custom
| way – you can use #[+api("displacy#render") #[code displacy.render]]
| instead. It works the same, but returns a string containing the markup.
+code("Example").
import spacy
from spacy import displacy
nlp = spacy.load('en')
doc1 = nlp(u'This is a sentence.')
doc2 = nlp(u'This is another sentence.')
html = displacy.render([doc1, doc2], style='dep', page=True)
p
| #[code page=True] renders the markup wrapped as a full HTML page.
| For minified and more compact HTML markup, you can set #[code minify=True].
| If you're rendering a dependency parse, you can also export it as an
| #[code .svg] file.
+aside("What's SVG?")
| Unlike other image formats, the SVG (Scalable Vector Graphics) uses XML
| markup that's easy to manipulate
| #[+a("https://www.smashingmagazine.com/2014/11/styling-and-animating-svgs-with-css/") using CSS] or
| #[+a("https://css-tricks.com/smil-is-dead-long-live-smil-a-guide-to-alternatives-to-smil-features/") JavaScript].
| Essentially, SVG lets you design with code, which makes it a perfect fit
| for visualizing dependency trees. SVGs can be embedded online in an
| #[code <img>] tag, or inlined in an HTML document. They're also
| pretty easy to #[+a("https://convertio.co/image-converter/") convert].
+code.
svg = displacy.render(doc, style='dep')
output_path = Path('/images/sentence.svg')
output_path.open('w', encoding='utf-8').write(svg)
+infobox("Important note")
| Since each visualization is generated as a separate SVG, exporting
| #[code .svg] files only works if you're rendering #[strong one single doc]
| at a time. (This makes sense – after all, each visualization should be
| a standalone graphic.) So instead of rendering all #[code Doc]s at one,
| loop over them and export them separately.
+h(2, "jupyter") Using displaCy in Jupyter notebooks
p
| If you're working with a #[+a("https://jupyter.org") Jupyter] notebook,
| you can use displaCy's "Jupyter mode" to return markup that can be
| rendered in a cell straight away. When you export your notebook, the
| visualizations will be included as HTML.
+code("Jupyter Example").
# don't forget to install a model, e.g.: python -m spacy download en
import spacy
from spacy import displacy
doc = nlp(u'Rats are various medium-sized, long-tailed rodents.')
displacy.render(doc, style='dep', jupyter=True)
doc2 = nlp(LONG_NEWS_ARTICLE)
displacy.render(doc2, style='ent', jupyter=True)
+image("/assets/img/docs/displacy_jupyter.jpg", 700, false, "Example of using the displaCy dependency and named entity visualizer in a Jupyter notebook")
p
| Internally, displaCy imports #[code display] and #[code HTML] from
| #[code IPython.core.display] and returns a Jupyter HTML object. If you
| were doing it manually, it'd look like this:
+code.
from IPython.core.display import display, HTML
html = displacy.render(doc, style='dep')
return display(HTML(html))
+h(2, "examples") Usage examples
+h(2, "manual-usage") Rendering data manually
p
| You can also use displaCy to manually render data. This can be useful if
| you want to visualize output from other libraries, like
| #[+a("http://www.nltk.org") NLTK] or
| #[+a("https://github.com/tensorflow/models/tree/master/syntaxnet") SyntaxNet].
| Simply convert the dependency parse or recognised entities to displaCy's
| format and import #[code DependencyRenderer] or #[code EntityRenderer]
| from #[code spacy.displacy.render]. A renderer class can be is initialised
| with a dictionary of options. To generate the visualization markup, call
| the renderer's #[code render()] method on a list of dictionaries (one
| per visualization).
+aside-code("Example").
from spacy.displacy.render import EntityRenderer
ex = [{'text': 'But Google is starting from behind.',
'ents': [{'start': 4, 'end': 10, 'label': 'ORG'}],
'title': None}]
renderer = EntityRenderer()
html = renderer.render(ex)
+code("DependencyRenderer input").
[{
'words': [
{'text': 'This', 'tag': 'DT'},
{'text': 'is', 'tag': 'VBZ'},
{'text': 'a', 'tag': 'DT'},
{'text': 'sentence', 'tag': 'NN'}],
'arcs': [
{'start': 0, 'end': 1, 'label': 'nsubj', 'dir': 'left'},
{'start': 2, 'end': 3, 'label': 'det', 'dir': 'left'},
{'start': 1, 'end': 3, 'label': 'attr', 'dir': 'right'}]
}]
+code("EntityRenderer input").
[{
'text': 'But Google is starting from behind.',
'ents': [{'start': 4, 'end': 10, 'label': 'ORG'}],
'title': None
}]
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